The better we come to understand the way intelligence develops in complex
systems in the universe, the more clearly we'll perceive our own role
and limits in fostering technological evolutionary development. Top-down
AI designers assume that human minds must furnish the most important goals
to our AI systems as they develop. Certainly some such goal-assignment
must occur, but it is becoming increasingly likely that this strategy
has rapidly diminishing marginal returns. Evolutionary developmental computation
(in both biological and technological systems) generally creates and discovers
its own goals and encodes learned information in its own
bottom-up, incremental, and context-dependent fashion, in a manner only
partially accessible to our rational analysis. Ask yourself, for example,
how much of your own mental learning has been due to inductive, trial-and-error
internalization of experience, and how much was a deductive, architected,
rationally-directed process. This topic, the self-organization of intelligence,
is observed in all complex systems to the extent that each system's phyics
allows, from molecules to minds.

In line with the new paradigm of evolutionary development of complex
systems, we are learning that tomorrow's most successful technological
systems must be organic in nature. Self-organization emerges only
through a process of cyclic development with limited evolution/variation
within each cycle, a self-replicating development that becomes incrementally
tuned for progressively greater self-assembly, self-repair, and self-reorganization,
particularly at the lowest component levels. At the same time, progressive
self-awareness (self-modelling) and general intelligence (environmental
modelling) are emergent features of such systems.

Most of today's technological systems are a long way from having these
capacities. They are rigidly modular, and do not adapt to or interdepend
with each other or their environment. They engage not in self-assembly,
but are mostly externally constructed. In discussing proteins, Michael
Denton reminds us of how far our technological systms have to go toward
this ideal. Living molecular systems engage extensively in the features
listed above. A proteins three dimensional shape is a result of a network
of local and nonlocal physical interdependences (e.g., covalent, electrostatic,
electrodynamic, steric, and solvent interactions). Both its assembly and
its final form are a developmentally computed emergent feature of that
interdependent network. A protein taken out of its interdependent milieu
soon becomes nonfunctional, as its features are a convergent property
of the interdependent system.

Today's artificial neural networks, genetic algorithms, and evolutionary
programs are promising examples of systems that demonstrate an already
surprising degree of self-replication, self-assembly, self-repair, and
self-reorganization, even at the component level. Implementing a hardware
description language genotype, which in turn specifys a hardware-deployed
neural net phenotype, and allowing this genotype-phenotype system to tune
for ever more complex, modular, and interdependent neural net emergence
is one future path likely to take us a lot further toward technological
autonomy. At the same time, as Kurzweil has argued, advances in human
brain scanning will allow us to instantiate ever more interdependent computational
architectures directly into the technological substrate, architectures
that the human mind will have less and less ability to model as we engage
in the construction process. In this latter example, human beings are
again acting as a decreasingly central part of the replication and variation
loop for the continually improving technological substrate.

Collective or "swarm" computation is also a critical element
of evolutionary development of complexity, and thus facilitating the emergence
of systems we only partially understand, but collectively utilize (agents,
distributed computation, biologically inspired computation), will be very
important to achieving the emergences we desire. Linking physically-based
self-replicating systems (SRS's) to the emerging biologically inspired
computational systems (neural networks, genetic algorithms, evolutionary
systems) which are their current predecessors will be another important
bottom up method, as first envisioned by John Von Neumann in the
1950's.

Physical SRS's, like today's primitive self-replicating
robots, provide an emerging body for the emerging mind of the coming
machine intelligence, a way for it to learn, from the bottom up the myriad
lessons of "common sense" interaction in the physical world
(e.g., sensorimotor before instinctual before linguistic
learning). As our simulation capacity, solid state physics, and fabrication
systems allow us to develop ever more functional micro, meso and nano
computational evolutionary hardware and evolutionary robotic SRS's in
coming decades (these will be functionally restricted versions of the
"general assembler" goal in nanotechnology) we may come
to view our technological systems simulation and fabrication capacity
as their "DNA-guided protein synthesis", their evolutionary
hardware and software as their emerging "nervous system" and
evolutionary robotics as the "body" of their emergent autonomous
intelligence.

At best, we conscious humans may create selection pressures which reward
for certain types of emergent complexity within the biologically inspired
computation/SRS environment. At the same time, all our rational striving
for a top down design and understanding of the AI we are now engaged in
creating will remain an important (though ever decreasing) part of the
process. Thus at this still-primitive stage of evolution of the coming
autonomous technologic substrate a variety of differentiated, not-yet-convergent
approaches to AI are to be expected. Comparing and contrasting the various
paths available to us, and choosing carefully how to allocate our resources
will be an essential part of humanity's role as memetically driven catalysts
of the coming transition.

In this spirit, let me now point out that on close inspection of the
present state of AI research, one finds that there are very few investigators
remaining who do not acknowledge the fundamental utility of evolution
as a creative component in future AI systems. Those nonevolutionary,
top-down AI approaches which still remain in vogue (whether classical
symbolic or one of the many historical derivatives of this) are now few
in number, and despite decades of iterative refinement, have consistently
demonstrated only minor incremental improvements in performance and functional
adaptation. To me, this is a strong indication that human-centric,
human-envisioned design has reached a "saturation phase" in
its attempt to add incremental complexity to technologic systems. We humans
simply aren't that smart, and the universe is showing us a much more powerful
way to create complexity than by trying to develop or deduce it from logical
first principles.

Thus we should not be surprised that on a human scale the handful of
researchers working on systems to encode some kind of "general intelligence"
in AI, after a surge of early and uneconomical attempts in the 1950's
to 1970's, now pale in comparison to the 50,000 or so computer scientists
who are investigating various forms of evolutionary computation. Over
the last decade we have seen a growing number of real theoretical and
commercial sucesses with genetic algorithms, genetic programming, evolutionary
strategies, evolutionary programming, and other intrinsically chaotic
and interdependent evolutionary computational approaches, even given
their current primitive encapsulation of the critical evolutionary
developmental aspects of genetic and neural computational systems and
their currently severe hardware and software complexity limitations.

We may therefore expect that the numbers of those funded investigators
who currently engage in this new evolutionary developmental paradigm will
continue to swell exponentially in coming decades, as they are following
what appears to be the most universally-permissive path to increasing
adaptive computational complexity.